At the same time, the rules of the financial markets are changing so rapidly that banks need to adapt quickly to remain relevant in the future.
The competitive threats of the internet giants are increasing and the expectations of customers, which are driven by their consumer-internet experiences, continue to grow.
Banks have to overcome the underlying conflict between the need to become faster, more agile, and flexible on the one hand and the scale, security standards, and regulatory requirements of a traditional bank on the other hand.
MLOps platforms like Domino can help banks to reinvent how they engage with their customers, create an AI-based decision-making culture, modernize their application infrastructure and establish a more agile operating model.
Learn how Data Scientists in banking can become faster and more productive.
See how automated monitoring can help banks make their data science models perform better.
Experience automated reproducibility of past results to address regulatory requirements for banks.
Understand how banks can increase collaboration in their AI teams.
Enterprise Account Executive at Domino Data Lab
Andreas has a Master's in Business Administration and more than 10 years of experience in Enterprise Software. He and his colleagues help large companies in the Banking, Insurance, Pharma, and Manufacturing industry to improve their Data Science outcomes by getting models into production faster, promoting collaboration, ensuring reproducibility of model results, and automating model monitoring.
Sales Engineer at Domino Data Lab
Petter Olsson joined Domino Data Lab as a Technical Support Engineer in 2018 and moved to Sales Engineering in the fall of 2021 and has over 20 years of experience in the industry working in 4 different countries across three continents. Previously, he worked as a Systems Administrator at the Max Planck Institute in Germany. Petter began his career at Enator in Sweden, working on Tandem's NonStop system and later in various roles worldwide. While living in India, he co-founded a data-driven fitness startup working with the Karnataka State Police and its anti-terrorist squad to optimize nutrition and training.
Case Study
Learn how this global financial services organization is transforming every part of its business using data science-from underwriting to customer service to human resources (HR).
Podcast
Chun Schiros, SVP, Head of Enterprise Data Science Group at Regions Bank, reveals how her team is leveraging AI solutions to optimize the banking experience.
Whitepaper
This whitepaper introduces a holistic, Enterprise MLOPs approach to scaling the production of models across modern enterprise financial organizations.
